# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/YouPig.py from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from typing import Dict, List from functools import reduce from pandas import DataFrame, DatetimeIndex, merge import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy # noqa class Github_remiotore_freqtrade__YouPig__20260111_210550(IStrategy): minimal_roi = { "0": 0.1 } stoploss = -0.035 trailing_stop = True trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.003 trailing_only_offset_is_reached = True ticker_interval = '5m' order_types = { 'buy': 'limit', 'sell': 'limit', 'emergencysell': 'market', 'stoploss': 'limit', 'stoploss_on_exchange': True, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } resample_factor = 12 resample_factor2 = 48 EMA_SHORT_TERM = 5 EMA_MEDIUM_TERM = 10 EMA_LONG_TERM = 20 EMA_XTRA_LONG_TERM = 200 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = self.resample(dataframe, self.ticker_interval, self.resample_factor) dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] = ta.EMA( dataframe, timeperiod=self.EMA_SHORT_TERM ) dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)] = ta.EMA( dataframe, timeperiod=self.EMA_MEDIUM_TERM ) dataframe['ema_{}'.format(self.EMA_LONG_TERM)] = ta.EMA( dataframe, timeperiod=self.EMA_LONG_TERM ) bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(dataframe), window=20, stds=2 ) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['min'] = ta.MIN(dataframe, timeperiod=self.EMA_MEDIUM_TERM) dataframe['max'] = ta.MAX(dataframe, timeperiod=self.EMA_MEDIUM_TERM) dataframe['cci'] = ta.CCI(dataframe) dataframe['mfi'] = ta.MFI(dataframe) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7) dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4 dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200) dataframe['adx'] = ta.ADX(dataframe, timeperiod=14) bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_middleband'] = bollinger['mid'] macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ dataframe.loc[ ( ( ( (dataframe['average'].shift(4) > dataframe['average'].shift(3)) &(dataframe['average'].shift(3) > dataframe['average'].shift(2)) & (dataframe['average'].shift(2) > dataframe['average'].shift(1)) & (dataframe['average'].shift(1) < dataframe['average'].shift(0)) & (dataframe['low'].shift(1) < dataframe['bb_middleband']) & (dataframe['cci'].shift(1) < -100) & (dataframe['rsi'].shift(1) < 30) & (dataframe['mfi'].shift(1) > 20) ) | ( (dataframe['close'] > dataframe['ema200']) &(dataframe['ema_{}'.format(self.EMA_SHORT_TERM, self.resample_factor2)] < dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM, self.resample_factor2)]) &(dataframe['ema_{}'.format(self.EMA_LONG_TERM, self.resample_factor2)] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM, self.resample_factor2)]) &(dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] < dataframe['ema_{}'.format(self.EMA_LONG_TERM)]) &(dataframe['ema_{}'.format(self.EMA_LONG_TERM)] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &(dataframe['close'].shift(0) > dataframe['open'].shift(0)) &(dataframe['close'].shift(1) < dataframe['open'].shift(1)) &(dataframe['ema20'] > dataframe['ema12']*1.0035) & (dataframe['low'].shift(1) < dataframe['bb_lowerband']) & (dataframe['low'] < dataframe['bb_lowerband']) & (dataframe['close'].shift(1) == dataframe['min']) ) ) & ( (dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20)) & (dataframe['resample_sma'] < dataframe['close']) & (dataframe['resample_sma'].shift(1) < dataframe['resample_sma']) ) ) , 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ dataframe.loc[ ( (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) & (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) & (dataframe['close'] >= dataframe['max']) & (dataframe['close'] >= dataframe['bb_upperband']) & (dataframe['mfi'] > 80) ) | ( (dataframe['open'] < dataframe['close']) & (dataframe['open'].shift(1) < dataframe['close'].shift(1)) & (dataframe['open'].shift(2) < dataframe['close'].shift(2)) & (dataframe['open'].shift(3) < dataframe['close'].shift(3)) & (dataframe['open'].shift(4) < dataframe['close'].shift(4)) & (dataframe['open'].shift(5) < dataframe['close'].shift(5)) & (dataframe['open'].shift(6) < dataframe['close'].shift(6)) & (dataframe['open'].shift(7) < dataframe['close'].shift(7)) & (dataframe['rsi'] > 70) ) , 'sell' ] = 1 return dataframe def resample(self, dataframe, interval, factor): df = dataframe.copy() df = df.set_index(DatetimeIndex(df['date'])) ohlc_dict = { 'open': 'first', 'high': 'max', 'low': 'min', 'close': 'last' } df = df.resample(str(int(interval[:-1]) * factor) + 'min', label="right").agg(ohlc_dict).dropna(how='any') df['resample_sma'] = ta.SMA(df, timeperiod=25, price='close') df = df.drop(columns=['open', 'high', 'low', 'close']) df = df.resample(interval[:-1] + 'min') df = df.interpolate(method='time') df['date'] = df.index df.index = range(len(df)) dataframe = merge(dataframe, df, on='date', how='left') return dataframe